similar to: constrained optimisation in R.

Hi, I'd like to make a legend with a mix of mathematical symbol (tau), numeric variable and character variables.I have tried : types<-c("Type 1","Type 2","Type 2") tau<-c(1,3,2) legend(x="topright",legend=paste(types,"tau=",expression(tau))) but it doesn't work: the 'tau' symbol is not written in its 'symbol

Dear kind R-experts. with miximum likelihood method i found the following estimations for my parameters vector: mu H A1 A2 . . . A10 D1 D2 . . . D10 but i want to add sum to zero constraints. sum(a1:a10)=0 and sum(d1:d10)=0 how i will do this? _________________________________________________________________ Show them the way! Add maps and directions to your party invites. [[alternative HTML

CRAN (and crantastic) updates this week New packages ------------ * bdoc (1.0) Michael Anderson http://crantastic.org/packages/bdoc This package contains a function that will classify DNA barcodes as well as a few test and reference data sets. * bdsmatrix (1.0) Terry Therneau http://crantastic.org/packages/bdsmatrix This is a special case of sparse matrices, used by coxme and

I want to get path profiling information with LLVM. LLVM provides methods for path profiling.I also get the llvmprof.out successfully. So I want to output the result.With llvm-prof ,I get the error:llvm-prof: Unkknown packet #5. So I have to write my own pass to output the path profiling result,the following is my kernel codes:

Hi all, I am new to R. I made this script that plots the deviation of a skellam-distributed random variable with respect to the skellam distribution. I would expect to get random errors, but the plot sistematically shows a non-random pattern (first a peak and then a low). I don't know how to explain this. Can somebody enlighten me?? require(skellam) n <- 5000 lam1 <- 5.45 lam2 <-

My code is as follows: #include <iostream> #include <cmath> using namespace std; #define MATHLIB_STANDALONE 1 extern "C" { #include "R_ext/Applic.h" } typedef struct TT{ double ** tempX; double * tempY; int tempN; } TT, *MM; double fn(int N, double * beta, void * ex){ double total = 0; int i = 0,j = 0; double * betaFn = new double[N]; MM tmp = (MM)ex;

Hi All, I'm trying to do the following constrained optimization example. Maximize x1*(1-x1) + x2*(1-x2) + x3*(1-x3) s.t. x1 + x2 + x3 = 1 x1 >= 0 and x1 <= 1 x2 >= 0 and x2 <= 1 x3 >= 0 and x3 <= 1 which are the constraints. I'm expecting the answer x1=x2=x3 = 1/3. I tried the "constrOptim" function in R and I'm running into some issues. I first start off

Hello, As a rails newbie, i have stumbled upon some tutorials promoting migratons. I have thought, wow, cool and started using them right away. However, now that i am reading the RoR list on a daily basis, i have seen also mails ranging from ''migrations are not always the best'' to ''migrations are evil'' and even ''i would not use migrations even

Hi all; I recently have been used 'maxLik' function for maximizing G2StNV178 function with gradient function gradlik; for receiving this goal, I write the following program; but I have been seen an error  in calling gradient  function; The maxLik function can't enter gradlik function (definition of gradient function); I guess my mistake is in line ******** ,that the vector  ‘h’ is

I''ve got a RoR app that will be going to production soon (in the next month or so) and would like to get some feedback on what kind of web server setup people have had good luck with. I''m looking for something that is: 1. Stable 2. Easy to setup 3. Secure (sorry but Windows is ruled out here) My deployment will either be on Fedora Core 5 or possible Mac OS X Server.

Dear R users, i would like to transform the following function from R-code to C-code and call it from R in order to speed up the computation because in my other functions this function is called many times. `dgcpois` <- function(z, lambda1, lambda2) { `f1` <- function(alpha, lambda1, lambda2) return(exp(log(lambda1) * (alpha - 1) - lambda2 * lgamma(alpha))) `f2` <-

All, Could anyone recommend a package that allows the user to constrain the coefficients from a multiple regression equation? I tried using the gl1ce function in lasso2, but couldn't get it to work. I created a contrived example to illustrate my starting point. data(cars) fmla <- formula(dist ~ speed) gl1c.E <- gl1ce(fmla, data = cars) gl1c.E gl1c.E <- gl1ce(fmla, data =

I would like to know if there is a way to "passthrought" the network interfaces that are phisically connected to dom0, eg, an additional pci card connected to the mb. I only understand a little about bridging, routing etc. Thank you a lot. -- View this message in context:

Dear All, I would like to minimise a nonlinear function subject to linear inequality constraints as part of an R program. I have been using the constrOptim function. I have tried all of the methods that come with Optim, but nothing finds the correct solution. If I use the correct solution as the vector of starting values, though, my program does output the correct solution and optimum - the

Dear list, The task view on optimization does not reference a package for non linear constrained optimization problems. Stefan Theussl told me to look at the Rsolnp package, but unfortunately it is not very clear what method is R ported. (The authors ported the matlab code of Yinyu Ye http://www.stanford.edu/~yyye/ <http://www.stanford.edu/%7Eyyye/>) Currently I'm looking for an

I'm having difficulty figuring out how to implement the following set of constraints in Quadprog: 1). x1+x2+x3+x4=a1 2). x1+x2+x5+x6=a2 3). x1+x3+x5+x7=a3 4). x1+x2=b1 5). x1+x3=b2 6). x1+x5=b3 for the problem: MIN (x1-c1)2+(x2-c2)2+...+(x8-c8)2. As far a I understand, "solve.QP(Dmat, dvec, Amat, bvec, meq=0, factorized=FALSE)" reads contraints using an element-by-element

Hi there, if anyone can please help it would be wonderful! I have a integer programming problem. I have a list of objects, and i need to select them according to certain contraints. In doing so, each object is given a value, and this value is what needs to be maximised. This is not the problem, i use a simple integer program(using lpSolve package), with a constraint matrix, setting all.bin =

Dear list, I was trying to use the R API for optimization method "Simulated annealing" void samin(int n, double *x, double *Fmin, optimfn fn, int maxit, int tmax, double temp, int trace, void *ex); but I encountered the following problem: The implementation of the function samin (as seen in src/main/optim.c) passes its void * argument "ex" into the function

I'm curious to find out if there is an example of R code for optimization of two variable function, with contraints, using lagrange multiplier (using optim/nlm?). I have a problem that contains one discrete variable, but need a simple problem/example to start with. I haven't been able to find any examples and thought I should ask here before I plunged into writing a few miles of R code.

How to convert the following ms() in Splus to Optim in R? The "Calc" function is also attached. ms(~ Calc(a.init, B, v, off, d, P.a, lambda.a, P.y, lambda.y, 10^(-8), FALSE, 20, TRUE)$Bic, start = list(lambda.a = 0.5, lambda.y = 240), control = list(maxiter = 10, tol = 0.1)) Calc <- function(A.INIT., X., V., OFF., D., P1., LAMBDA1., P2., LAMBDA2., TOL., MONITOR.,